PTSD affects approximately 14% of OEF/OIF Veterans and leads to considerable personal and societal costs (e.g., increased morbidity, reduced work productivity, poorer relationships). Although cognitive behavioral therapy (CBT) is one of the most effective treatments for PTSD, a substantial portion (approximately 50%) of individuals drop out prematurely, do not respond to treatment, or relapse. Treatment engagement is worse for OEF/OIF Veterans, who attend fewer sessions and have higher dropout rates than civilians and Veterans from other eras. One likely barrier to treatment engagement and effectiveness is the executive functioning problems present in individuals with PTSD. Executive functions (EFs) are the set of higher-level cognitive skills that organize and integrate lower-level cognitive processes in order to perform complex, goal-directed tasks. PTSD has been associated with EF deficits, including impairments in inhibitory control, working memory, and cognitive flexibly, as well as dysfunction in a network of brain regions that support EFs (e.g., prefrontal cortex [PFC], cingulate). EFs are essential for CBT in order to engage the cognitive skills involved in treatment (e.g., self-monitoring, inhibiting distorted thoughts, flexibly generating/evaluating alternative thoughts). This is particularly true for Cognitive Processing Therapy (CPT), a frontline CBT treatment for PTSD, which involves identifying and challenging maladaptive trauma-related thoughts to alter their impact on emotions and behavior. Thus, EF deficits may lead to reduced CPT engagement and responsivity. In fact, worse EF at baseline has been associated with poorer response to CBT in several disorders (e.g., generalized anxiety disorder, obsessive compulsive disorder, schizophrenia). Further, a study of brain functioning during an EF task demonstrated that dysfunction in EF-related brain regions including PFC and cingulate cortex at baseline predicted nonresponse to CBT for PTSD. Directly targeting EF prior to CPT via cognitive training would strengthen executive networks and likely boost treatment effectiveness, allowing Veterans to fully engage in and benefit more from components of CPT (e.g., cognitive restructuring). Evidence suggests that computerized cognitive training improves EF and functioning in EF-related brain regions, increases mental health treatment completion rates, and enhances response to CBT, though this has not yet been tested in a PTSD population. Thus, the main goal of the proposed study is to examine whether administering computerized EF training (CEFT) immediately prior to CPT will improve executive functioning and enhance treatment adherence, completion rates, and psychological and functional outcomes in OEF/OIF Veterans with PTSD. Objective (neuropsychological) and subjective (self- report) measures of EF will be collected to determine if CEFT enhances EF and if this in turn mediates the relationship between treatment condition and PTSD symptom improvement. Functional neuroimaging during EF tasks will also be collected at baseline to determine whether functioning within an EF network predicts treatment response, above and beyond traditional paper-and-pencil measures of EF. Veterans (N=110) will be randomized to either 12 weeks of CEFT-CPT or a placebo word training condition plus CPT. Assessments will be administered at baseline, immediately after CEFT or word training (prior to CPT), and after CPT completion. The proposed research aims to reduce barriers to treatment engagement and has potential to significantly enhance current treatments for PTSD by combining cognitive and psychotherapeutic approaches. Targeting EF directly and independently represents a logical, innovative, and empirically-informed method for augmenting existing treatments for PTSD in order to optimize outcomes. Findings from the proposed study will not only directly inform clinical practice, but also have the potential to significantly improve the quality of Veterans? lives, reduce societal costs and burden, improve access to care, and reveal ways to better match individuals with treatments they are most likely to benefit from.